Research Network Research Articles

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database. Keywords such as diabetic kidney disease, hemodialysis, insurance or big data, and prediction were employed. Inclusion criteria were original articles and review articles written in English published between 2010 and 2022. VOSviewer and the Bibliometrix package in R were used for comprehensive bibliometric analysis. VOSviewer facilitated keyword co-occurrence analysis to identify clusters and visualize relationships among keywords, emphasizing distinct research themes, keyword density, and network visualization. Meanwhile, Bibliometrix allowed exploration of key metrics such as prolific authors and institutions, publication trends, co-authorship networks, citations, document types, emerging trends through keyword analysis, and network visualizations, including co-authorship and keyword co-occurrence. Results from both tools were integrated for a thorough analysis. The present study yielded 2,199 articles, which was reduced to 1,828 after removing duplicates and applying inclusion criteria. This bibliometric analysis found that machine learning and artificial intelligence are emerging yet remain relatively under-researched in the context of hemodialysis and DKD. The prominence of topics such as diabetic nephropathy, non-insulin treatments, and lifestyle modifications highlighted ongoing research priorities in DKD and hemodialysis. Taiwan's dominance in publications suggested robust research activity in this field, while international collaboration underscored global interest and the potential for diverse research perspectives. The need for similar research development in Indonesia, leveraging big data and machine learning, indicates opportunities for advancing the understanding and management of DKD and hemodialysis within the region.

Large language models recover scientific collaboration networks from text

Science is a collaborative endeavor. Yet, unlike co-authorship, interactions within and across teams are seldom reported in a structured way, making them hard to study at scale. We show that Large Language Models (LLMs) can solve this problem, vastly improving the efficiency and quality of network data collection. Our approach iteratively applies filtering with few-shot learning, allowing us to identify and categorize different types of relationships from text. We compare this approach to manual annotation and fuzzy matching using a corpus of digital laboratory notebooks, examining inference quality at the level of edges (recovering a single link), labels (recovering the relationship context) and at the whole-network level (recovering local and global network properties). Large Language Models perform impressively well at each of these tasks, with edge recall rate ranging from 0.8 for the highly contextual case of recovering the task allocation structure of teams from their unstructured attribution page to 0.9 for the more explicit case of retrieving the collaboration with other teams from direct mentions, showing a 32% improvement over a fuzzy matching approach. Beyond science, the flexibility of LLMs means that our approach can be extended broadly through minor prompt revision.

Scoping review of epigenetics on neurodegenerative diseases: research frontiers and publication status.

Epigenetics has significantly evolved and emerged as important players in the pathogenesis of neurodegenerative diseases. However, a scientometric synthesis of such changes over time is currently lacking. We conducted a comprehensive search of the Web of Science Core Collection from inception until November 5, 2022, using appropriate keywords. Our primary objective was to employ scientometric analysis to depict changes in keywords over time and to assess the structure and credibility of clusters. Additionally, we examined the network of research (countries, institutions, and authors) using CiteSpace and VOSviewer. We identified 25 clusters with well-structured networks (Q = 0.82) and highly credible clustering (S = 0.91) from 16,181 articles published between 1999 and 2022. Our findings are as follows: (a) the literature and research interest concerning the epigenetics of neurodegenerative diseases are continuously growing; (b) the three most productive countries are the USA, China, and Germany; (c) international collaborative relationships exist, alongside small, isolated collaboration networks of individual institutions. The number and impact of global publications on the epigenetics of neurodegenerative diseases have expanded rapidly over the past 20 years. This review provides valuable guidelines for researchers interested in neurodegenerative diseases research.

Neural Network Algorithm for Training a Mobile Robot in the Task of Following a Target

The article is devoted to the research, development and implementation of an artificial neural network with reinforcement for controlling a mobile robot with a differential drive to solve the task of following a target. The authors of the study present a detailed description of the architecture of the neural network, its training using a deep deterministic gradient policy algorithm, and integration with the robot control system. A distributed neural network structure was selected for specialized generation of controls, for which the authors chose the angular and linear speed of the robot. A system of rules has been synthesized that takes into account changes in the distance between the robot and the target object and tracking of the heading angle in real time. A mathematical model of a robot with a differential drive is considered, on the basis of which a simulation program is implemented for intermediate training of a neural network, as a result of which the initial weighting coefficients are formed. Using this program, you can get by with less energy and time costs at the initial stage of the study. Experiments to test the effectiveness of the developed neural network were carried out in the Gazebo simulation environment using the ROS2 communication interface. The article describes the process of integrating a neural network with a robot control system in a simulation environment, as well as test results and analysis of the obtained data. The results of experiments are presented for three scenarios in which the initial position of the robot is the same, the remaining parameters are generated according to the rules described by the authors. The effectiveness of using such a solution for trajectory planning has been confirmed. The research contributes to the field of autonomous systems and demonstrates the potential of artificial reinforcement neural networks in robotics.

International collaboration as a turning point for sudden cardiac arrest prediction and treatment: the PARQ COST Action

International collaboration as a turning point for sudden cardiac arrest prediction and treatment: the PARQ COST Action PARQ aims to create a network of researchers across Europe whose overall aim is to reduce the societal burden of sudden cardiac arrest (SCA). To reach this aim, PARQ will conduct studies that focus on two dimensions: (1) prevention of SCA through the discovery of its inherited, acquired, and environmental causes, and their interaction, and improved recognition of individuals at risk; and (2) improvement of survival chances after SCA through the development and implementation of resuscitation treatments in the community.

Mpox in People With Human Immunodeficiency Virus: Predictors of Diagnosis, Outcomes, and Vaccine Effectiveness in a Multisite Cohort.

Since its global reemergence in 2022, monkeypox (mpox) has demonstrated increased incidence and severity among people with human immunodeficiency virus (HIV [PWH]). Predictors of mpox diagnosis, vaccination, and outcomes among PWH are limited. We included PWH with primary care visits after 1 January 2022 at 9 US sites participating in the Centers for AIDS Research Network of Integrated Clinic Systems Network. We identified mpox diagnosed between 1 June 2022 and 31 May 2023, through a combination of polymerase chain reaction result, diagnosis code, and/or tecovirimat receipt. We examined validated clinical diagnoses, laboratory results, vaccine data, and patient reported outcomes. We evaluated relative risks (RR) of mpox diagnosis, hospitalization, tecovirimat treatment, and vaccine receipt. Among 19 777 PWH in care, 413 mpox cases (all male sex at birth) occurred (2.2 cases/100 person-years). Age <40 years, geographic region, Hispanic/Latine ethnicity, lack of antiretroviral therapy, detectable HIV viral load, and recent bacterial sexually transmitted infection predicted mpox diagnosis. PWH with CD4 200-349 cells/mm3 were most likely to be hospitalized (adjusted RR, 3.20; 95% confidence interval: 1.44-7.09) compared to CD4 ≥500, but half as likely as those with CD4 <200 to receive tecovirimat. Overall, smallpox/mpox vaccine effectiveness of ≥1 vaccine was 71% (adjusted RR, 0.29; 95% confidence interval: .14-.47) at preventing mpox, and 86% or better with CD4 ≥350 or HIV viral suppression. Non-Hispanic Black PWH were less likely to be vaccinated than other racial/ethnic identities. PWH not on antiretroviral therapy or with unsuppressed HIV were more likely to be diagnosed with, and hospitalized for, mpox. Mpox/smallpox vaccine effectiveness was high, inclusive of those with low CD4 count and HIV viremia.

Survey of the endoscopy workforce’s perception of sustainability

ObjectiveThe National Health Service is a significant contributor to greenhouse gas emissions, with endoscopy ranking as the third highest contributor of waste in healthcare. We aimed to ascertain the endoscopy...

Early-onset preeclampsia/gestational hypertension may be associated with a low incidence of cerebral palsy at 3 years old in singleton very low-birth-weight infants born at 28-31 weeks of gestation (EOPE-DQ study): a multi-center retrospective cohort study in 2013-2016.

Our aim was to evaluate the effects of any types of hypertensive disorders of pregnancy (HDP) on the development of either cerebral palsy (CP) or developmental delay (DD) at 3 years old in singleton very low-birth-weight (VLBW) infants born at 24-31 weeks of gestation. This was a retrospective cohort study of VLBW infants born at 24-31 weeks in 2013-2016 in Japan, using a nationwide obstetrical database, and Neonatal Research Network Japan (NRNJ) Database, accompanied by a secondary survey of women complicated with HDP (EOPE-DQ study). In 529 candidates for long-term follow-up in 7 tertiary centers, the percentage undergoing follow-up for CP at 3 years old was 56.1%, and the percentage receiving follow-up for DD at 3 years old was 54.1%. The percentage of PE/SPE/GH was significantly lower in infants with CP than in controls (1/22 [4.5%] vs. 66/274 [24.1%], p = 0.034); especially, in infants born at 28-31 weeks, the percentage of PE/SPE/GH was significantly lower in infants with CP than in controls (0/13 [0%] vs. 44/151 [29.1%], p = 0.021). The percentage of PE/SPE/GH was not different between infants with DD and controls (9/49 [18.4%] vs. 54/237 [22.8%], p = 0.574). The percentage of composite risk factors (either bronchopulmonary dysplasia at a postmenstrual age of 36 weeks, intraventricular hemorrhage, hypoxic ischemic encephalopathy, sepsis, necrotizing enterocolitis, or periventricular leukomalacia) was significantly higher in infants with DD than in controls. In conclusion, PE/SPE/GH around 30 weeks may be associated with a low incidence of CP.

Automatic Processing of Bibliometric Data: Newest Literature Analysis Algorithms

Automated processing of bibliometric data has become an integral element in understanding and measuring the impact of scientific research as well as trends in the academic literature. The purpose of this study was to evaluate and compare current literature analysis algorithms to identify advances in automated processing of bibliometric data. This study explores the use of the latest algorithms in literature analysis which includes identification of research themes, research network analysis, citation impact evaluation, and development of literature recommendation algorithms. This research exposes innovative applications that utilize the latest technologies in text processing and data analysis to assist researchers, libraries, and information professionals in revealing deep insights from the latest scientific literature. The results of this study illustrate that the automated processing of bibliometric data with the latest algorithms paves the way for a deeper understanding of the development of scientific research, its impact in the scientific community as well as improving bibliometric analysis methods and tools by integrating the latest algorithms, so that scientific research and trends can be identified more accurately and relevantly. The study could use more sophisticated multifaceted techniques to analyze simple productivity measures based on article citations. This research is also able to recognize and describe the development of new science and technology.

Inferring the dust emission at submillimeter and millimeter wavelengths using neural networks

The Planck mission provided all-sky dust emission maps in the submillimeter (submm) to millimeter (mm) range at an angular resolution of 5$^ prime $. In addition, some specific sources can be observed at long wavelengths and higher resolution using ground-based telescopes. These observations are limited to small scales and are sometimes not delivered to the community. These ground-based observations require extensive data processing before they become available for scientific analysis, and suffer from extended emission filtering. At present, we are still unable to fully understand the emissivity variations observed in different astrophysical environments at long (submm and mm) wavelengths. Several models have been developed to reproduce the diffuse Galactic medium, and each distinct environment requires an adjustment of the models. It is therefore challenging to estimate any dust emission in the submm-mm at a better resolution than the 5$^ prime $ from Planck . In this analysis, based on supervised deep learning algorithms, we produced dust emission predictions in the two Planck bands centered at 850 mic (353 GHz) and 1.38 mm (217 GHz) at the Herschel resolution (37$^ prime $). Prediction or forecasting is a frequently used term in machine learning or neural network research that refers to the output of an algorithm that has been trained on a given dataset and that is being used for modeling purposes. Herschel data of Galactic environments, ranging from 160 mic to 500 mic and smoothed to an angular resolution of 5$^ prime $, were used to train the neural network. This training aimed to provide the most accurate model for reproducing Planck maps of dust emission at 850 mic and 1.38 mm. Then, using Herschel data only, the model was applied to predict dust emission maps at 37$^ prime The neural network is capable of reproducing dust emission maps of various Galactic environments with a difference of only a few percent at the Planck resolution. Remarkably, it also performs well for nearby extragalactic environments. This could indicate that large dust grains, probed by submm or mm observations, have similar properties in both our Galaxy and nearby galaxies, or at least that their spectral behaviors are comparable in Galactic and extragalactic environments. For the first time, we provide to the community dust emission prediction maps at 850 mic and 1.38 mm at the 37$^ prime $ of several surveys: Hi-GAL, Gould Belt, Cold Cores, HERITAGE, Helga, HerM33es, KINGFISH, and Very Nearby Galaxies. The ratio of these two wavelength brightness bands reveals a derived emissivity spectral index statistically close to 1 for all the surveys, which favors the hypothesis of a flattened dust emission spectrum for wavelengths larger than 850 mic . Neural networks appear to be powerful algorithms that are highly efficient at learning from large datasets and achieving accurate reproductions with a deviation of only a few percent. However, to fully recover the input data during the training, it is essential to sample a sufficiently large range of datasets and physical conditions.

Demographics, management and outcomes of major trauma in older patients at an Irish trauma unit.

Major trauma data in the developed world trends towards increased rates of lower energy mechanism in an older cohort. This study examines how the mechanism of trauma, injury pattern and outcomes differ between a younger and older cohorts in an Irish trauma unit. In this retrospective study, all entries to the Trauma Audit and Research Network (TARN) database from Tallaght University Hospital, between January 1st 2016 and 31st December 2021, were interrogated. Differences between injury severity, 30-day mortality, and hospital length of stay in younger and older patients were also examined. Older patients (≥ 65years) had a similar severity of injury to younger patients (< 65years); however, they had longer stays in hospital (18.8 versus 9.5days, p < 0.001). Older patients (≥ 65years) were more likely to have head (16.1% versus 11.3%, p < 0.05) and cervical spine (21.4% versus 11.2%, p < 0.05) injuries as their primary injury compared with younger patients. Older patients (≥ 65years) are less likely to have limb (33.3% versus 45.6%, p < 0.05). Fall from a height less than 2m was more likely to be the mechanism of injury in the older cohort compared with the younger cohort (77.3% versus 36.3%, p < 0.05). This study highlights the increased mortality and morbidity experienced by older trauma patients compared with a younger cohort. The findings support the development of "silver trauma" care pathways and directing resources to meet the needs of older trauma patients and optimize clinical outcomes.

Eigenmode and eigenpropagation of the electromagnetic waves in Möbius and Klein networks

To explore the distribution of characteristic frequencies and the propagation properties of eigenmodes in topological networks at the zero-energy level, we design optical waveguide networks with two typical topologies: Möbius network and Klein network, inspired by the Möbius strip and Klein bottle, respectively. We investigate the degeneracy at characteristic frequencies and the propagation properties of the eigenmodes of these networks, both theoretically and experimentally. We discovered an intriguing eigenpropagation in the Möbius network and multiple degenerate eigenmodes in the Klein network, analyzing the propagation characteristics and distribution patterns of electromagnetic waves within them. In our experiments, we utilize coaxial cables as one-dimensional waveguides to construct transmission line networks for the two networks. We observe the distinct transmission paths of the Möbius network’s eigenmode and the two degenerate eigenmodes of the Klein network. Our findings provide a theoretical foundation for new optical modal transmission devices and novel nanoarrays, with potential implications for theoretical and experimental research in other quantum systems and topological networks.

Phenotyping Men With Lower Urinary Tract Symptoms: Results From the Symptoms of Lower Urinary Tract Dysfunction Research Network.

Men with lower urinary tract symptoms (LUTS) represent a heterogeneous group, and treatment decisions are often based on severity of symptoms and physical examination findings. Identification of clinically meaningful subtypes could allow for more personalized care. This study advances phenotyping efforts from the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) by adding data domains to previous phenotyping using urologic symptoms alone. Two-hundred-seventeen LUTS, demographics, medical history, and physical examination datapoints from the LURN Observational Cohort study were assessed among 519 men with at least one bothersome LUTS, using weighted Tanimoto indices, semi-supervised learning, and resampling-based consensus clustering to identify distinct clusters of participants. Differentially abundant serum proteins of 220 men were compared across identified clusters. Five refined male clusters (RM1-RM5) were identified. Two clusters reported mild LUTS (RM1: n = 66; RM2: n = 84). RM1 was older than RM2 (70.3 vs. 56.1 years), had more comorbidities (functional comorbidity index 2.4 vs. 1.5) and erectile dysfunction. Two benign prostatic hyperplasia-like symptom clusters were identified (RM3: n = 64; RM4: n = 188). RM3 has the largest postvoid residual volume (275 mL); RM4 reported more urinary frequency, urgency, urinary incontinence, pain, and psychosocial symptoms. RM5 (n = 119) was characterized by urgency urinary incontinence, frequency, and significant comorbidities and psychosocial symptoms. Fifteen (RM2) to 87 (RM1) differentially abundant proteins were identified within each cluster. Minimal overlap was observed between affected proteins and pathways across clusters. Protein signatures across newly discovered subgroups suggest identified subtypes are biochemically distinct. Findings should be validated, but may represent populations with separate pathophysiology and therapeutic needs. The LURN ClinicalTrials.gov Identifier is NCT02485808.

Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data

Green technologies such as solar photovoltaic systems and electric vehicles play a fundamental role in the global decarbonization effort. To enable their diffusion, electricity distribution networks need to be upgraded, which is a complex and expensive endeavor. However, utilities face budgetary constraints and seek to reduce planning uncertainty. Here, we utilize 7,527 real-world grid topologies and land cover data to develop a model for estimating conductor and capital investment needs for electrifying a specific area. Our work yields three main contributions: First, we demonstrate the important role of land cover data in power line planning. We show that, for medium and large networks, distinct methodologies are needed due to the significant impact of land cover, particularly buildings and roads. Second, we introduce a parsimonious model of power line length and identify the number of consumption points as the primary determinant of network investment costs. Third, we present a cost assessment model tailored for regulators and investors, offering valuable insights for network planning, policymaking, due diligence, and research. Our work highlights the importance of combining land cover data and operations research algorithms in distribution network planning and provides policymakers with a tool to ensure cost-efficient network expansion.

Organizational Climate and its Impact in a Women’s Prison on the Mental Health of Correctional Staff

The purpose of this study was to explore the organizational climate in a women’s prison from the shared perceptions of correctional staff. This study was part of the Prison Research and Innovation Network (PRIN) project, which began in the fall of 2020. Forty-two correctional staff from a low-medium security women’s prison in the Midwest were interviewed in spring 2021. Thematic analysis was conducted to identify staff’s perceptions of organizational climate in the women’s correctional facility. The themes identified as most important to staff were mental health, correctional practices, job satisfaction, and workplace culture. An item pool was developed from the themes identified, and a newly developed organizational climate survey was administered to 80 correctional staff. Qualitative data point to the issues of staff shortages and mandated overtime work impacting the mental health of staff. In addition, organizational climate and its impact on mental health were examined through multiple logistic regressions. The results indicated that having job promotion opportunities, having daily communication between staff and administration, and staff’s knowledge and awareness of correctional policies were positively associated with mental health. This mixed-methods study contributes to understanding the unique challenges correctional staff face working in women’s correctional facilities.

Insights and networks: methodological assessment and scientometric analysis of economic evaluations in dentistry

IntroductionAssessing the methodological quality of economic evaluations (EEs) is crucial for evidence-based decision-making. The study aimed to evaluate EEs in restorative dentistry and endodontics, while also analyzing the scientific landscape of researchers and publications through co-authorship and citation network analysis providing an insight into the distribution of scientific expertise.MethodologyA systematic search for relevant articles from 2012 to 2022 was conducted using PubMed, Scopus, and EBSCO. The ten-point Drummond checklist was used to appraise the methodological quality of included studies. Bibliometric data for network analysis were extracted from the Dimensions database and visualized using VOSviewer software.ResultsOf the 37 articles, 81.08% scored good, 16.21% average, and 2.7% poor on the methodological rating scale. Most of the included studies were in Q1 journals, with limited representation in Q2 and Q3 journals. Compliance was highest in Q2 journals (95%), followed by Q1 (88.36%), while it dropped to 40% for Q3 journals. Co-authorship analysis revealed a dense network of researchers, with Prof. Falk Schwendicke V. having a significant influence. Moreover, the Journal of Dentistry had the highest impact, followed by Journal of Endodontics and BMC Oral Health.ConclusionsDespite a diverse scientific landscape, participation from developing countries was limited emphasizing the need for inclusivity and diversity in the scientific network. While the quantity of good-quality studies was encouraging, the overall quality of evidence remains paramount for decision-making in healthcare policy and practice. Therefore, continuous efforts to improve methodological rigor and reporting practices are essential to contribute robust evidence.

A Comparative Effectiveness Study of Speech and Surgical Outcomes: Study Overview.

To provide an overview of the Cleft Outcomes Research NETwork (CORNET) and the CORNET Speech and Surgery study. The study is (1) comparing speech outcomes and fistula rate between two common palate repair techniques, straight-line closure with intra-velar veloplasty (IVVP) and Furlow Double-Opposing Z-palatoplasty (Furlow Z-plasty); (2) summarizing practice variation in the utilization of early intervention speech-language (EI-SL) services; and (3) exploring the association between EI-SL services and speech outcomes. Prospective, longitudinal, observational, comparative effectiveness, multi-center. Twenty sites across the United States. One thousand two hundred forty-seven children with cleft palate with or without cleft lip (CP ± L). Children with submucous cleft palate or bilateral sensorineural severe to profound hearing loss were excluded from participation. Straight-line closure with IVVP or Furlow Z-plasty based on each surgeon's standard clinical protocol. The primary study outcome is perceptual ratings of hypernasality judged from speech samples collected at 3 years of age. Secondary outcomes are fistula rate, measures of speech production, and quality of life. The statistical analyses will include generalized estimating equations with propensity score weighting to address potential confounders. Recruitment was completed in February 2023; 80% of children have been retained to date. Five hundred sixty two children have completed their final 3-year speech assessment. Final study activities will end in early 2025. This study addresses long-standing questions related to the effectiveness of the two most common palatoplasty approaches and describes CORNET which provides an infrastructure that will streamline future studies in all areas of cleft care.

Risk of Venous Thromboembolism in Adolescents Undergoing Pelvic Osteotomy: Insights From a Propensity-matched Retrospective Cohort Study.

Pelvic osteotomies are surgical procedures that are commonly performed in adolescents and young adults to improve stability or correct various deformities. Venous thromboembolism (VTE), including pulmonary embolism (PE) and deep vein thrombosis (DVT), are dreaded complications of any major procedure including pelvic osteotomies. Unlike adults, the incidence of DVT and need for prophylactic measures are not well understood in the adolescents. The purpose of this study is to understand the need for VTE prophylaxis in adolescents and determine if their risk profile aligns with adults, who have more established VTE prophylactic guidelines in place for these surgeries. This retrospective cohort study utilized data from the TriNetX Research Network, which includes records from over 80 healthcare organizations and more than 120 million patients. De-identified patient data from January 1, 2003, to March 1, 2024, were extracted using relevant ICD-9 and ICD-10 procedural codes. The study included 2 cohorts: patients aged 13 to 17 years (adolescents) and patients 18 years and older (adults) who underwent pelvic osteotomies. To control for confounding variables, propensity score matching was employed based on sex and relevant comorbidities, including diabetes mellitus, tobacco use, and overweight/obesity. Statistical significance was set at P<0.01. A total of 2374 patients successfully matched in each cohort. Within 90 days following surgical intervention, adult patients had overall risks of 4.5%, 1.9%, and 5.9%, whereas adolescent patients had overall risks of 1.3%, 0.5%, and 1.4% for DVT (RR: 3.419), PE (RR: 4.182), and either DVT or PE (RR: 4.118), respectively. This study examines the rate of VTE in a large cohort of adolescents undergoing pelvic osteotomies. Although we found the rates to be lower in adolescents compared with adults, the high absolute risk in adolescents-above 1%, the threshold established in the literature for considering prophylaxis-underscores the need for tailored risk stratification strategies and targeted thromboprophylaxis protocols for this population. Level III: case-control study or retrospective cohort study.

Risk of Acute Complications with Rocuronium versus Cisatracurium in Patients with Chronic Kidney Disease: A Propensity-Matched Study.

Neuromuscular blocking agents (NMBAs) play an integral role in modern anesthesia by facilitating endotracheal tube placement, assisting with mechanical ventilation, and creating optimal surgical conditions. However, NMBAs can have deleterious side effects. The purpose of this study was to retrospectively analyze acute complications of 2 pharmacodynamically similar but pharmacokinetically different NMBAs and their respective reversal agents. The global research network database, TriNetX, was used to evaluate deidentified patient information from 63 health care organizations. Cohort A was defined as patients aged 18 to 80 years who had chronic kidney disease (CKD) and were administered rocuronium with sugammadex. Cohort B was defined as patients aged 18 to 80 years who had CKD and were administered cisatracurium with neostigmine. Cohorts were propensity matched for age at event, ethnicity, race, sex, and relevant confounding pathologies. All outcomes besides mortality were analyzed from the same day to 1 week after administration of the indexed drug. Mortality was analyzed from the same day to thirty days after administration of the indexed drug. A total of 95,740 patients with CKD-administered rocuronium with sugammadex were matched with 10,708 patients with CKD-administered cisatracurium with neostigmine. Patients administered rocuronium with sugammadex had a significantly higher associated risk of respiratory failure (risk ratios [RR], 1.98, confidence interval [CI], 1.71-2.29, P < .0001), acute respiratory distress (RR, 2.70, CI, 1.31-5.58, P = .0052), hypertensive crisis (RR, 1.85, CI, 1.37-2.49, P < .0001), heart failure (RR, 1.14, CI, 1.06-1.23, P = .0004), pleural effusion (RR, 1.30, CI, 1.14-1.49, P < .0001), and 30-day mortality (RR, 1.31, CI, 1.10-1.56, P = .0021). From 2003 to 2023, patients who were administered rocuronium plus sugammadex were at a significantly higher risk for acute cardiovascular and pulmonary complications when compared to patients who were administered cisatracurium plus neostigmine.

Cybersecurity Testbeds for IoT: A Systematic Literature Review and Taxonomy

Researchers across the globe are carrying out numerous experiments related to cybersecurity, such as botnet dispersion, intrusion detection systems powered by machine learning, and others, to explore these topics in many different contexts and environmental settings. One current research topic is the behavior of Internet of Things (IoT) devices, as they increasingly become a common feature of homes, offices, and companies.. Network testing environments which are designated as testbeds, are boosting the effectiveness of network research. However, exploratory studies in IoT cybersecurity may include a wide range of requirements. This article seeks to carry out a survey of IoT cybersecurity testbeds. A critical systematic literature review was conducted to select relevant articles, by applying a novel taxonomy to classify the testbeds. The surveyed testbeds are classified in terms of their primary target domain and other features such as fidelity, heterogeneity, scalability, security, reproducibility, flexibility, and measurability. Furthermore, we have compared the testbeds with regard to each feature. Thus, the main contribution made by this study lies in a) the insights it provides into the state-of-the-art in IoT cybersecurity testbeds, and b) the emphasis laid on the main benefits and limitations that were found in the surveyed testbeds.